Robotics & Machine Learning Daily News2024,Issue(Oct.4) :111-111.

Findings from Hefei Provide New Insights into Robotics (Research on accurate mor phology predictive control of CFETR multi-purpose overload robot)

Robotics & Machine Learning Daily News2024,Issue(Oct.4) :111-111.

Findings from Hefei Provide New Insights into Robotics (Research on accurate mor phology predictive control of CFETR multi-purpose overload robot)

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Abstract

By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators discuss new findings in robotics. According to news originating from Hefei, People's Republic of China, by NewsRx editors, the research stated, "The CFETR multipurpose overload robot ( CMOR) is a critical component of the fusion reactor remote handling system." Our news correspondents obtained a quote from the research from Department of In formation Engineering: "To accurately calculate and visualize the structural def ormation and stress characteristics of the CMOR motion process, this paper first establishes a CMOR kinematic model to analyze the unfolding and working process in the vacuum chamber. Then, the dynamic model of CMOR is established using the Lagrangian method, and the rigid-flexible coupling modeling of CMOR links and j oints is achieved using the finite element method and the linear spring damping equivalent model. The co-simulation results of the CMOR rigid-flexible coupled m odel show that when the end load is 2000 kg, the extreme value of the end-effect or position error is more than 0.12 m, and the maximum stress value is 1.85 x 10 8 Pa. To utilize the stress-strain data of CMOR, this paper designs a CMOR morph ology prediction control system based on Unity software. Implanting CMOR finite element analysis data into the Unity environment, researchers can monitor the st ress strain generated by different motion trajectories of the CMOR robotic arm i n the control system."

Key words

Department of Information Engineering/H efei/People's Republic of China/Asia/Emerging Technologies/Machine Learning/Robot/Robotics

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出版年

2024
Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

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